NPTEL Deep Learning For Computer Vision Assignment 5 Answers 2022

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NPTEL Deep Learning For Computer Vision Assignment 5 Answers 2022 – Here All The Questions and Answers Provided to Help All The Students and NPTEL Candidate as a Reference Purpose, It is Mandetory to Submit Your Weekly Assignment By Your Own Understand Level.

Are you looking for the Assignment Answers to NPTEL Deep Learning For Computer Vision Assignment 5 Answers 2022? If Yes You are in Our Great Place to Getting Your Solution, This Post Should be help you with the Assignment answer to the National Programme on Technology Enhanced Learning (NPTEL) Course “NPTEL Deep Learning For Computer Vision Assignment 5 Answers 2022”

NPTEL Deep Learning For Computer Vision

ABOUT THE COURSE :

The automatic analysis and understanding of images and videos, a field called Computer Vision, occupies significant importance in applications including security, healthcare, entertainment, mobility, etc. The recent success of deep learning methods has revolutionized the field of computer vision, making new developments increasingly closer to deployment that benefits end users. This course will introduce the students to traditional computer vision topics, before presenting deep learning methods for computer vision. The course will cover basics as well as recent advancements in these areas, which will help the student learn the basics as well as become proficient in applying these methods to real-world applications. The course assumes that the student has already completed a full course in machine learning, and some introduction to deep learning preferably, and will build on these topics focusing on computer vision.

This course can have Associate in Nursing unproctored programming communication conjointly excluding the Proctored communication, please check announcement section for date and time. The programming communication can have a weightage of twenty fifth towards the ultimate score.

Final score = Assignment score + Unproctored programming exam score + Proctored Exam score
  • Assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
  • ( All assignments in a particular week will be counted towards final scoring – quizzes and programming assignments). 
  • Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam – out of 100
  • Proctored Exam score =50% of the proctored certification exam score out of 100
YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF ASSIGNMENT SCORE >=10/25 AND
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50. 
If any one of the 3 criteria is not met, you will not be eligible for the certificate even if the Final score >= 40/100. 

CHECK HERE OTHERS NPTEL ASSIGNMENTS ANSWERS 

BELOW YOU CAN GET YOUR NPTEL Deep Learning For Computer Vision Assignment 5 Answers 2022? :

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1.Starter code for Question 1 to Question 3 of this assignment is provided in DL4CV-Assignment-5-2022.ipynb.
Use Python 3.x to run the notebook. As instructed in the notebook, write your code only in between the lines ’YOUR CODE STARTS HERE’ and ’YOUR CODE ENDS HERE’.
Do not change anything else in the code; if you do, the answers you are supposed to get at the end of this assignment might be wrong.
Read documentation of each function carefully.
All the best!

1 point
For this question, please see Question 1 in the iPython notebook (.ipynb file) provided alongside. Complete your implementation under the “YOUR CODE STARTS HERE” segment therein. What is the size of the parameter matrix corresponding to convolution layer of the second sequential block?
32 × 16 × 5 × 5
32 × 32 × 6 × 6
16 × 32 × 7 × 7
32 × 16 × 4 × 4

Ans  – C
1 point
2. For this question, please see Question 2 in the iPython notebook (.ipynb file) provided alongside. Complete your implementation under the “YOUR CODE STARTS HERE” segment therein. Report the final test accuracy displayed above (If you are not getting the exact number shown in options, please report the closest number).
84%
76%
99%
57%
Ans  B
1 point
3. For this question, please see Question 3 in the iPython notebook (.ipynb file) provided alongside. Complete your implementation under the “YOUR CODE STARTS HERE” segment therein. Report the final test accuracy displayed above (If you are not getting the exact number shown in options, please report the closest number).
85%
73%
54%
65%
Ans  C
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4) Given an input image of shape (10,10,3), you want to use one of the two following layers:

• Fully connected layer with 2 neurons, with biases
• Convolutional layer with three 2×2 filters (with biases) with 0 padding and a stride of 2.

If you use the fully-connected layer, the input volume is “flattened” into a column vector before being fed into the layer. What is the difference in the number of trainable parameters between these two layers?
(a) The fully connected layer has 566 fewer parameters
(b) The convolutional layer has 518 fewer parameters
(c) The convolutional layer has 563 fewer parameters
(d) None of the above

Ans  D

5) Which of the following statements are true? Select all possible answers:
(a) The number of learnable parameters in pooling layer is 0.
(b) Dilation rate is same as stride.
(c) One pixel is affected in a convolved output by a single input pixel when even-sized filters are used.

(d) Derivative of loss w.r.t. input image X, where Y=X∗W is ∂L∂X[ij]=∂L/∂Y(⊕W), where ∗ represents convolution and ⊕ represents correlation.

Ans  B

6) You are solving a binary classification task of classifying images as dog vs not-dog. You design a CNN with a single output neuron. Let the output of this neuron be z. The final output of your network, y^ is given by:
y^=σ(ReLU(z))
You classify all inputs with a final value y^≥0.5 as dog. State whather the following statement is true or false:
“Using ReLU followed by sigmoid in the output layer will cause all predictions to be positive.”
(a) True
(b) False

Ans  B

7) Parameter sharing (i.e. a feature detector that is useful for one part of the image is probably useful for another part of the image too) is one of the benefits of using convolutional networks. Among the following options given below, select the true statements about parameter sharing in ConvNets. Select all possible answers:
(a) It reduces the total number of parameters, thus reducing overfitting
(b) It allows a features detector to be used in multiple locations throughout the whole input image/input volume
(c) It allows gradient descent to set many of the parameters to zero, thus making the connection sparse
(d) None of the above

Ans  C

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8) Which of the following statements on Residual Networks are true? (Select all correct answers)
(a) The skip-connection makes it easy for the network to learn an identity mapping between the input and the output within a ResNet block
(b) The skip-connections compute a complex non-linear function of the input to pass to a deeper layer in the network
(c) Using a skip-connection helps the gradient to backpropagate to earlier layers in substantial amounts, and thus helps you train deeper networks
(d) None of the above

Ans  A

9)Weight sharing allows CNNs to deal with image data without using too many parameters. Select the correct option from below with respect to weight sharing.
(a) Weight sharing increases bias
(b) Weight sharing increases variance
(c) It increases both bias and variance
(d) None of the above

Ans  B

10) Sort the following CNN architectures in descending order based on number of parameters.
(a) AlexNet > VGGNet19 > EfficientNetB0 > ResNet50
(b) VGGNet19 > AlexNet > EfficientNetB0 > ResNet50
(c) VGGNet19 > AlexNet > ResNet50 > EfficientNetB0
(d) AlexNet > ResNet50 > VGGNet19 > EfficientNetB0
(e) AlexNet > VGGNet19 > ResNet50 > EfficientNetB0

Ans  C

11) Sort the following CNN architectures in ascending order based on their top-1 accuracy on Imagenet.
(a) AlexNet < VGGNet11 < ResNet101 < EfficientNetB7
(b) AlexNet < VGGNet11 < EfficientNetB7 < ResNet101
(c) VGGNet11 < AlexNet < EfficientNetB7 < ResNet101
(d) ResNet101 < AlexNet < EfficientNetB7 < VGGNet11

Ans  C

Let the input to a convolutional layer be of size Df×Df×M where Df=100 and M=8, and the output feature map (after passing input through conv layer) be of size Df×Df×N where N=32. Assume padded convolution. Let width of the square kernel in conv layer be k where k=7 (Ignore the bias term in the calculation). Calculate the number of parameters and computational cost for this convolution layer.
13) Number of Parameters: ______

Ans  32

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14) Computational Cost: _______

Ans 312

Using the same dimensions specified in the previous question, calculate the number of parameters and computational cost, but make use of Depthwise Seperable convolution instead of standard convolution.
15) Number of parameters for depthwise convolution:______

Ans= 31

16) Computational Cost for for depthwise convolution: ______

Ans= 310

17) Number of parameters for pointwise convolution: ______

Ans= 30

18) Computational cost for for pointwise convolution: ______

Ans= 320

If we convolve a feature map of size 128 × 128 × 12 with a filter of size 11 × 11 × 5, and a stride of 2 across all dimensions without any padding, the width of the output volume is a and the depth of the output volume is b .
19) a: ______

Ans= 120

20) b: ______

Ans= Not Sure

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